Thermal-Induced Multi-State Memristors for Neuromorphic Engineering

Ren Li, Sonal Shreya, Saverio Ricci, Davide Bridarolli, Daniele Ielmini, Hooman Farkhani, Farshad Moradi

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review


With the rapidly evolving internet of things (IoT) era, the ever-rising demand for data transfer and storage has put a knotty problem on conventional computers, known as the von Neumann bottleneck and memory wall problem. Slow scaling of CMOS transistors due to physical and economical limitations further exacerbates the situation. It is only logical to mimic what has been known so far as the most energy-efficient system, the human brain. The brain-inspired neuromorphic computing systems compute and store the data locally, which dramatically reduces area and energy consumption. In this work, we demonstrate thermal-induced multi-state memristors for neuromorphic engineering applications. We show that in a neural network that uses a memristor-spintronic nano oscillator connection to implement the synapse-neuron pair, with increased temperature, the total power consumption could be reduced by more than 50 % without degrading the output power of a spintronic-based neuron.

Original languageEnglish
Title of host publicationIEEE International Symposium on Circuits and Systems (ISCAS) : Proceedings
Number of pages5
Place of publicationMonterey
Publication dateJul 2023
ISBN (Electronic)9781665451093
Publication statusPublished - Jul 2023
SeriesProceedings - IEEE International Symposium on Circuits and Systems


  • memristors
  • neuromorphic computing
  • neuromorphics
  • resistive RAM


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